{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from operator import itemgetter\n",
    "import profile_common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = profile_common.load_data()\n",
    "df = df.drop(['o_max_creationdate','i_install_max_date','o_max_orderdate','o_num_90_nqh','o_num_30_nqh'],1)\n",
    "# 逾期发生后，禁止下单，需要排除最大下单时间这个关联指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Users\\qiang.qian\\AppData\\Local\\Continuum\\Anaconda3\\lib\\site-packages\\ipykernel\\__main__.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------1\n",
      "('i_install_min_date', 0.19627840530854232)\n",
      "('o_min_orderdate', 0.10436660160610246)\n",
      "('o_num_90', 0.078863200463005198)\n",
      "('o_num_30', 0.066260017119782036)\n",
      "('o_htl_min_amount', 0.039080099459989442)\n",
      "('o_num_180_nqh', 0.038041916272234459)\n",
      "('o_htl_num_nqh', 0.032402404821055247)\n",
      "('o_avg_amount', 0.030377075482254684)\n",
      "('o_min_creationdate', 0.025554867050007073)\n",
      "('o_num_nqh', 0.021931778293010191)\n",
      "preds        0      1\n",
      "actual               \n",
      "0       110761  26227\n",
      "1          448    819\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.81      0.89    136988\n",
      "          1       0.03      0.65      0.06      1267\n",
      "\n",
      "avg / total       0.99      0.81      0.88    138255\n",
      "\n"
     ]
    },
    {
     "data": {
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VBxe1LVHS09M1MzMzjFMnpe7dYcECm/HB70FJIisL1qyxYt6GDfDTT7boh3Mh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\ngQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15417128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------2\n",
      "('i_install_min_date', 0.19185350534371037)\n",
      "('o_num_90', 0.11291097874202201)\n",
      "('o_min_orderdate', 0.11063443831695252)\n",
      "('o_num_nqh', 0.044878591265413929)\n",
      "('o_flt_min_amount', 0.038775774381032489)\n",
      "('o_num_30', 0.029579972545682089)\n",
      "('o_num_180_nqh', 0.02867416681147)\n",
      "('o_create_time', 0.027979811567276282)\n",
      "('o_htl_min_amount', 0.023502719257432671)\n",
      "('o_days_use', 0.023249106109839164)\n",
      "preds        0      1\n",
      "actual               \n",
      "0       111030  25958\n",
      "1          430    837\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.81      0.89    136988\n",
      "          1       0.03      0.66      0.06      1267\n",
      "\n",
      "avg / total       0.99      0.81      0.89    138255\n",
      "\n"
     ]
    },
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T6dFHw47G7SInB1atgiOOsO5ov/wChx8edlSughORaaqaWZbnxtI99i9FbLui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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b781390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------3\n",
      "('i_install_min_date', 0.18506074745499199)\n",
      "('o_min_orderdate', 0.11359413543442694)\n",
      "('o_num_90', 0.10707071362205833)\n",
      "('o_num_180_nqh', 0.038931135284275123)\n",
      "('o_num_nqh', 0.034633921208115072)\n",
      "('o_create_time', 0.030967898999494592)\n",
      "('o_num_30', 0.02649347007155517)\n",
      "('o_min_creationdate', 0.026271170327138566)\n",
      "('o_htl_min_amount', 0.024122579828320986)\n",
      "('o_avg_amount', 0.023102100791690547)\n",
      "preds        0      1\n",
      "actual               \n",
      "0       111720  25268\n",
      "1          438    829\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.82      0.90    136988\n",
      "          1       0.03      0.65      0.06      1267\n",
      "\n",
      "avg / total       0.99      0.81      0.89    138255\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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rrz4S0oVORCaqakZJnhtLh8o/FbDt+pKczJWO0aMtQdx2myeJpLJrl9UHNmsG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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1526bda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------4\n",
      "('i_install_min_date', 0.18775256925364242)\n",
      "('o_min_orderdate', 0.12481111467496157)\n",
      "('o_num_90', 0.1018499705928121)\n",
      "('o_num_30', 0.049129462192010295)\n",
      "('o_create_time', 0.030700667721271512)\n",
      "('o_avg_amount_nqh', 0.029875031602071972)\n",
      "('o_num_nqh', 0.028851522207901121)\n",
      "('o_num_180_nqh', 0.028788120507537068)\n",
      "('o_min_creationdate', 0.027451649648120337)\n",
      "('o_avg_amount', 0.02742816757455057)\n",
      "preds        0      1\n",
      "actual               \n",
      "0       112576  24412\n",
      "1          450    817\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.82      0.90    136988\n",
      "          1       0.03      0.64      0.06      1267\n",
      "\n",
      "avg / total       0.99      0.82      0.89    138255\n",
      "\n"
     ]
    },
    {
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kKqlYR2YfjU0zPl9EmgEj4xuWK/Dee7BggZcmEmb5cjj7bDjtNNhrL1tr1huG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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x165654a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------5\n",
      "('i_install_min_date', 0.20749707458896066)\n",
      "('o_num_90', 0.11409140103375517)\n",
      "('o_min_orderdate', 0.10165230964590181)\n",
      "('o_min_creationdate', 0.043537167568806309)\n",
      "('o_avg_amount', 0.030395395115287844)\n",
      "('o_htl_num', 0.02442757859158157)\n",
      "('o_htl_num_nqh', 0.024330106040501905)\n",
      "('o_num_30', 0.024003262237508885)\n",
      "('o_num_180_nqh', 0.023090079873113328)\n",
      "('o_days_use', 0.02283169033520931)\n",
      "preds        0      1\n",
      "actual               \n",
      "0       112516  24472\n",
      "1          460    807\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.82      0.90    136988\n",
      "          1       0.03      0.64      0.06      1267\n",
      "\n",
      "avg / total       0.99      0.82      0.89    138255\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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BDFbV6SU5X3nTt68tHnTddWFH4qJ6+mmrYnroIbjrLpt8y7kUI6rRq/xFpKeq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\nFM4556L6f4Nql5k6+4s0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6f7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------6\n",
      "('i_install_min_date', 0.18511576090092435)\n",
      "('o_min_orderdate', 0.094073578209699499)\n",
      "('o_num_90', 0.07620684008601776)\n",
      "('o_num_30', 0.06770112622047765)\n",
      "('o_num_180_nqh', 0.038460483267405289)\n",
      "('o_min_creationdate', 0.037052916400567057)\n",
      "('o_htl_min_amount', 0.035118344310638795)\n",
      "('o_num_nqh', 0.033596780852994702)\n",
      "('o_avg_amount_nqh', 0.029303481439451665)\n",
      "('o_htl_num_nqh', 0.027731950407786005)\n",
      "preds        0      1\n",
      "actual               \n",
      "0       110931  26057\n",
      "1          429    838\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.81      0.89    136988\n",
      "          1       0.03      0.66      0.06      1267\n",
      "\n",
      "avg / total       0.99      0.81      0.89    138255\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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09DBOXaL88Qcceiicdx4MGRJ2NG4PGRnw++9wzDE2uOXnn22VKOdCJCJTVTW1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\nnHMuKk8Uzjnnovp/nI113ADqZTQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe8c6be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------7\n",
      "('i_install_min_date', 0.20448924495255025)\n",
      "('o_num_90', 0.11999157387072329)\n",
      "('o_min_orderdate', 0.11604755059723935)\n",
      "('o_min_creationdate', 0.036887406805512717)\n",
      "('o_num_nqh', 0.035193413559506684)\n",
      "('o_num_30', 0.029023410046579757)\n",
      "('o_num_180_nqh', 0.029000894967694178)\n",
      "('o_avg_amount', 0.026825452182407727)\n",
      "('o_flt_min_amount', 0.025021152510258837)\n",
      "('o_htl_num_nqh', 0.024107112994436623)\n",
      "preds        0      1\n",
      "actual               \n",
      "0       111291  25697\n",
      "1          448    819\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.81      0.89    136988\n",
      "          1       0.03      0.65      0.06      1267\n",
      "\n",
      "avg / total       0.99      0.81      0.89    138255\n",
      "\n"
     ]
    },
    {
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8+KLVivMkkSJ27bKSG61a2RyyqjYBiCcJl+YKbXoSkQuxfoUm+fokagAbEx2Y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\nz9htIEBWYRQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xff0eba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------8\n",
      "('i_install_min_date', 0.18987020513757683)\n",
      "('o_num_90', 0.12196086391868009)\n",
      "('o_min_orderdate', 0.10712422098354414)\n",
      "('o_num_180_nqh', 0.048292277490229608)\n",
      "('o_num_nqh', 0.033799766530386903)\n",
      "('o_avg_amount_nqh', 0.027373176770702477)\n",
      "('o_min_creationdate', 0.026031864477884747)\n",
      "('o_num_30', 0.024991835689750462)\n",
      "('o_htl_avg_amount', 0.024553370234963588)\n",
      "('o_create_time', 0.023362020253360784)\n",
      "preds        0      1\n",
      "actual               \n",
      "0       110326  26662\n",
      "1          410    857\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.81      0.89    136988\n",
      "          1       0.03      0.68      0.06      1267\n",
      "\n",
      "avg / total       0.99      0.80      0.88    138255\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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q1eGBBzxJuKwVq+kpMt1po1QE4uIzeDDssIM1h7sQTJpkdzfOmgXnnefzRLgq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\nyROFc865mP4f6jF0zI1h8KsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11419c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------9\n",
      "('o_num_90', 0.1848665801547105)\n",
      "('o_min_orderdate', 0.11378857456454709)\n",
      "('i_install_min_date', 0.10269498540171255)\n",
      "('o_num_180_nqh', 0.043766462469439604)\n",
      "('o_htl_num_nqh', 0.042628751539336832)\n",
      "('o_num_30', 0.041102876724232393)\n",
      "('o_avg_amount', 0.031144510625193519)\n",
      "('o_num_nqh', 0.024163886595585621)\n",
      "('o_days_use', 0.02407099790343999)\n",
      "('o_min_creationdate', 0.023810959006574331)\n",
      "preds        0      1\n",
      "actual               \n",
      "0       110726  26262\n",
      "1          412    855\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.81      0.89    136988\n",
      "          1       0.03      0.67      0.06      1267\n",
      "\n",
      "avg / total       0.99      0.81      0.88    138255\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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5xWzzyo4QRdacOPZYTxIpZft2m3KjY0c491ybxK9ePU8SLu2VWPUkImdg7Qpt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\npv8H5W2B4onn1SgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3b2509b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preds        0      1\n",
      "actual               \n",
      "0       119227  17761\n",
      "1          357    910\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       1.00      0.87      0.93    136988\n",
      "          1       0.05      0.72      0.09      1267\n",
      "\n",
      "avg / total       0.99      0.87      0.92    138255\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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QptiYRH1gfbwDS3cFlaPvvjvsSFyVmD/fTnWdMAGOPRZGjfIifi5tRBujyMbm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\n1f8DvZpS7hyW5/EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xfb8f3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X,y = profile_common.prepare_data(df,['o','i'],'d',False)\n",
    "\n",
    "train_X, test_X, train_y, test_y = train_test_split(X,y, test_size=0.5, random_state=0)\n",
    "\n",
    "train_X['y_d'] = train_y\n",
    "df_train = train_X \n",
    "\n",
    "batch = profile_common.balance_batch_data(df_train,['o','i'],'d',9)\n",
    "\n",
    "i = 1\n",
    "vote_y = test_X.copy()\n",
    "for (X,y) in batch:\n",
    "    clf = DecisionTreeClassifier()\n",
    "    clf.fit(X, y)\n",
    "    print('------------------' + str(i))\n",
    "    for feature in sorted(zip(list(X.columns), clf.feature_importances_),key=itemgetter(1),reverse=True)[:10]:\n",
    "        print(feature)\n",
    "    preds = clf.predict(test_X)\n",
    "    profile_common.score(test_y,preds)\n",
    "    pred_i = 'pred_' + str(i)\n",
    "    vote_y[pred_i] = preds\n",
    "    i = i + 1\n",
    "    \n",
    "pred_c = list(filter(lambda x:x.startswith('pred_'),vote_y.columns.tolist()))\n",
    "\n",
    "vote_y['vote_sum'] = (vote_y[pred_c]).apply(lambda x:x.sum(),axis=1)\n",
    "\n",
    "vote_y['vote'] = vote_y['vote_sum'] >= (len(pred_c) + 1)/2\n",
    "\n",
    "vote_y = vote_y.replace({True:1,False:0})\n",
    "\n",
    "profile_common.score(test_y,vote_y['vote'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
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